Sr. Engineer, Machine Learning

Dayforce

1d•$127,800 - $228,100•Remote

About The Position

Dayforce is a global human capital management (HCM) company headquartered in Toronto, Ontario, and Minneapolis, Minnesota, with operations across North America, Europe, Middle East, Africa (EMEA), and the Asia Pacific Japan (APJ) region. Our award-winning Cloud HCM platform offers a unified solution database and continuous calculation engine, driving efficiency, productivity and compliance for the global workforce. Our brand promise - Makes Work Life Better™ - Reflects our commitment to employees, customers, partners and communities globally. This posting represents an ongoing opportunity within our organization. While there may not be an active vacancy at this time, we encourage interested candidates to apply. Applications will be reviewed periodically and retained for future openings. We are seeking an experienced and talented Senior Machine Learning Engineer to join our ML team. As a Senior Machine Learning Engineer, you will work on delivering ML components for innovative products such as Dayforce AI Assistant and Dayforce Agents. This role involves rapidly designing, implementing, evaluating, and maintaining machine learning models, algorithms, APIs, and software systems in a fast succeed/fast fail environment. You will contribute both as a hands-on engineer and as a technical leader, helping guide solutions from prototype through production while ensuring performance, scalability, reliability, and maintainability.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Statistics, or a related field. Equivalent practical experience will also be considered.
6+ years of overall software development experience.
2+ years of experience in machine learning and AI software development.
Strong programming experience in Python and machine learning frameworks such as TensorFlow, PyTorch, Keras, or Scikit-learn.
Experience building APIs and backend services using frameworks such as Flask or FastAPI.
Experience with frontend development using React.
Strong understanding of supervised and unsupervised learning, deep learning, reinforcement learning, NLP, ensemble methods, and ML fundamentals.
Familiarity with AI fairness concepts and bias detection/mitigation techniques.
Experience with cloud platforms such as AWS, Azure, or GCP and their machine learning services.
Experience working with relational and non-relational databases, including MSSQL, NoSQL, Delta Tables, and related technologies.
Strong understanding of data structures, algorithms, design patterns, and scalable software architecture.
Experience with CI/CD pipelines, Docker containers, and cloud-based ML deployment workflows.
Strong analytical, problem-solving, and critical-thinking skills.
Excellent communication and collaboration skills, with the ability to explain complex technical concepts to technical and non-technical stakeholders.

Nice To Haves

Experience with data visualization, feature engineering, and data manipulation techniques.
Practical experience detecting and mitigating model bias using fairness-aware ML techniques.
Experience building and deploying ML-powered products in production environments.
Experience with AWS cloud infrastructure and tooling.
Contributions to open-source ML projects or a portfolio of previous ML work.
If you are highly motivated, thrive in fast-paced environments, and enjoy rapidly applying new and existing AI techniques to solve evolving problems, this role may be a great fit for you. You will work across both generative AI applications using unstructured data and AI solutions leveraging structured enterprise data.

Responsibilities

Design, develop, and implement machine learning models, algorithms, and API services that meet business needs and requirements.
Develop full-stack solutions including frontend, middle-tier, and backend components using technologies such as React, Python, SQL, Delta Tables, GraphQL, and PySpark.
Apply machine learning techniques to large datasets to identify trends, patterns, and actionable insights.
Collaborate with cross-functional teams including software developers, data scientists, data engineers, and domain experts to prototype and productionize AI-driven solutions.
Prepare, clean, and preprocess large-scale datasets to ensure high data quality and suitability for training ML models.
Evaluate and optimize machine learning models for accuracy, efficiency, scalability, and bias mitigation.
Identify and analyze potential biases in datasets, features, and model predictions, implementing fairness and mitigation strategies where appropriate.
Manage end-to-end machine learning pipelines, from data preprocessing and feature engineering through training, deployment, monitoring, and continuous improvement.
Deploy and integrate machine learning models into production environments and implement monitoring systems for usage, performance, and feedback collection.
Develop and maintain ML software systems, reusable libraries, testing frameworks, and automated test suites.
Participate in research and development of emerging machine learning and AI technologies.
Mentor junior engineers and contribute technical leadership within the team.
Stay current with advancements in machine learning, AI technologies, cloud infrastructure, and software engineering best practices.